Advanced Seismic Imaging for Accurate Segmentation of Salt Regions Using U-Net Architecture

The segmentation of salt-affected lands is a critical task, as the presence of salt in soil significantly hinders water absorption, rendering these lands unsuitable for agriculture and leading to slower plant growth. This project aims to accurately identify and segment regions containing salt using advanced seismic imaging techniques. To achieve this, we employ the U-Net architecture, which features a dual-path design: the contraction path, responsible for capturing the context of the image, and the symmetric expanding path, which ensures precise localization through the use of transposed convolutions. This design retains essential features from the input images, thereby enhancing the accuracy of salt region segmentation. Our methodology involves classifying pixels as either salt or sediment based on their seismic image characteristics. We utilize convolutional neural networks (CNNs) to perform the semantic segmentation of salt bodies within seismic volumes. These segmented salt bodies provide critical insights, such as the extraction of seismic horizons and other valuable geophysical information. The contributions of this work are twofold: firstly, it demonstrates the effectiveness of CNNs as a supportive tool for geophysical interpreters, facilitating more accurate and efficient analysis. Secondly, it explores the data requirements necessary to achieve reliable predictions by the network. This research underscores the significant potential of advanced AI techniques in geophysical applications, aiming to improve the efficiency and precision of salt segmentation tasks in seismic imagery analysis.